Detection of Crop Damage in Maize Using Red–Green–Blue Imagery and LiDAR Data Acquired Using an Unmanned Aerial Vehicle
Crop damage caused by wild animals, particularly wild boars (<i>Sus scrofa</i>), significantly impacts agricultural yields, especially in maize fields. This study evaluates two methods for assessing maize crop damage using UAV-acquired data: (1) a deep learning-based approach employing t...
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2025-01-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/1/238 |
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| author | Barbara Dobosz Dariusz Gozdowski Jerzy Koronczok Jan Žukovskis Elżbieta Wójcik-Gront |
| author_facet | Barbara Dobosz Dariusz Gozdowski Jerzy Koronczok Jan Žukovskis Elżbieta Wójcik-Gront |
| author_sort | Barbara Dobosz |
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| description | Crop damage caused by wild animals, particularly wild boars (<i>Sus scrofa</i>), significantly impacts agricultural yields, especially in maize fields. This study evaluates two methods for assessing maize crop damage using UAV-acquired data: (1) a deep learning-based approach employing the Deepness plugin in QGIS, utilizing high-resolution RGB imagery; and (2) a method based on digital surface models (DSMs) derived from LiDAR data. Manual visual assessment, supported by ground-truthing, served as the reference for validating these methods. This study was conducted in 2023 in a maize field in Central Poland, where UAV flights captured high-resolution RGB imagery and LiDAR data. Results indicated that the DSM-based method achieved higher accuracy (94.7%) and sensitivity (69.9%) compared to the deep learning method (accuracy: 92.9%, sensitivity: 35.3%), which exhibited higher precision (92.2%) and specificity (99.7%). The DSM-based method provided a closer estimation of the total damaged area (9.45% of the field) compared to the reference (10.50%), while the deep learning method underestimated damage (4.01%). Discrepancies arose from differences in how partially damaged areas were classified; the deep learning approach excluded these zones, focusing on fully damaged areas. The findings suggest that while DSM-based methods are well-suited for quantifying extensive damage, deep learning techniques detect only completely damaged crop areas. Combining these methods could enhance the accuracy and efficiency of crop damage assessments. Future studies should explore integrated approaches across diverse crop types and damage patterns to optimize wild animal damage evaluation. |
| format | Article |
| id | doaj-art-695d61a9bd8c4905a7bb4f197973b06f |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Agronomy |
| spelling | doaj-art-695d61a9bd8c4905a7bb4f197973b06f2025-01-24T13:17:17ZengMDPI AGAgronomy2073-43952025-01-0115123810.3390/agronomy15010238Detection of Crop Damage in Maize Using Red–Green–Blue Imagery and LiDAR Data Acquired Using an Unmanned Aerial VehicleBarbara Dobosz0Dariusz Gozdowski1Jerzy Koronczok2Jan Žukovskis3Elżbieta Wójcik-Gront4Department of Biometry, Institute of Agriculture, Warsaw University of Life Sciences, Nowoursynowska 159, 02-776 Warsaw, PolandDepartment of Biometry, Institute of Agriculture, Warsaw University of Life Sciences, Nowoursynowska 159, 02-776 Warsaw, PolandAgrocom Polska, Strzelecka 47, 47-120 Żędowice, PolandDepartment of Business and Rural Development Management, Vytautas Magnus University, 53361 Kaunas, LithuaniaDepartment of Biometry, Institute of Agriculture, Warsaw University of Life Sciences, Nowoursynowska 159, 02-776 Warsaw, PolandCrop damage caused by wild animals, particularly wild boars (<i>Sus scrofa</i>), significantly impacts agricultural yields, especially in maize fields. This study evaluates two methods for assessing maize crop damage using UAV-acquired data: (1) a deep learning-based approach employing the Deepness plugin in QGIS, utilizing high-resolution RGB imagery; and (2) a method based on digital surface models (DSMs) derived from LiDAR data. Manual visual assessment, supported by ground-truthing, served as the reference for validating these methods. This study was conducted in 2023 in a maize field in Central Poland, where UAV flights captured high-resolution RGB imagery and LiDAR data. Results indicated that the DSM-based method achieved higher accuracy (94.7%) and sensitivity (69.9%) compared to the deep learning method (accuracy: 92.9%, sensitivity: 35.3%), which exhibited higher precision (92.2%) and specificity (99.7%). The DSM-based method provided a closer estimation of the total damaged area (9.45% of the field) compared to the reference (10.50%), while the deep learning method underestimated damage (4.01%). Discrepancies arose from differences in how partially damaged areas were classified; the deep learning approach excluded these zones, focusing on fully damaged areas. The findings suggest that while DSM-based methods are well-suited for quantifying extensive damage, deep learning techniques detect only completely damaged crop areas. Combining these methods could enhance the accuracy and efficiency of crop damage assessments. Future studies should explore integrated approaches across diverse crop types and damage patterns to optimize wild animal damage evaluation.https://www.mdpi.com/2073-4395/15/1/238crop damagewild boar<i>Sus scrofa</i>maizeUAVRGB imagery |
| spellingShingle | Barbara Dobosz Dariusz Gozdowski Jerzy Koronczok Jan Žukovskis Elżbieta Wójcik-Gront Detection of Crop Damage in Maize Using Red–Green–Blue Imagery and LiDAR Data Acquired Using an Unmanned Aerial Vehicle Agronomy crop damage wild boar <i>Sus scrofa</i> maize UAV RGB imagery |
| title | Detection of Crop Damage in Maize Using Red–Green–Blue Imagery and LiDAR Data Acquired Using an Unmanned Aerial Vehicle |
| title_full | Detection of Crop Damage in Maize Using Red–Green–Blue Imagery and LiDAR Data Acquired Using an Unmanned Aerial Vehicle |
| title_fullStr | Detection of Crop Damage in Maize Using Red–Green–Blue Imagery and LiDAR Data Acquired Using an Unmanned Aerial Vehicle |
| title_full_unstemmed | Detection of Crop Damage in Maize Using Red–Green–Blue Imagery and LiDAR Data Acquired Using an Unmanned Aerial Vehicle |
| title_short | Detection of Crop Damage in Maize Using Red–Green–Blue Imagery and LiDAR Data Acquired Using an Unmanned Aerial Vehicle |
| title_sort | detection of crop damage in maize using red green blue imagery and lidar data acquired using an unmanned aerial vehicle |
| topic | crop damage wild boar <i>Sus scrofa</i> maize UAV RGB imagery |
| url | https://www.mdpi.com/2073-4395/15/1/238 |
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